Inferring both 3D structure and motion of nonrigid objects from monocular images is an important problem in computer vision. The challenges stem not only from the absence of point correspondences but also from the str...
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Inferring both 3D structure and motion of nonrigid objects from monocular images is an important problem in computer vision. The challenges stem not only from the absence of point correspondences but also from the structure ambiguity. In this paper, a hierarchical method which integrates both local patch analysis and global shape descriptions is devised to solve the dual problem of structure and nonrigid motion recovery by using an elastic geometric model-extended superquadrics (ESQ). The nonrigid object of interest is segmented into many small areas and local analysis is performed to recover small details for each small area, assuming that each small area is undergoing similar nonrigid motion. Then, a recursive algorithm is proposed to guide and regularize local analysis with global information by using an appropriate global ESQ model. This local-global hierarchy enables us to capture both local and global deformations accurately and robustly. Experimental results on both simulation and real data are presented to validate and evaluate the effectiveness and robustness of the proposed approach.
We present a novel approach to estimate and analyze 3D fluid structure and motion of clouds from multi-spectrum 2D cloud image sequences. Accurate cloud-top structure and motion are very important for a host of meteor...
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We present a novel approach to estimate and analyze 3D fluid structure and motion of clouds from multi-spectrum 2D cloud image sequences. Accurate cloud-top structure and motion are very important for a host of meteorological and climate applications. However, due to the extremely complex nature of cloud fluid motion, classical nonrigid motion analysis methods are insufficient for solving this particular problem. In this paper, two spectra of satellite cloud images are utilized. The high-resolution visible channel is first used to perform cloud tracking by using a recursive algorithm which integrates local motion analysis with a set of global fluid constraints, defined according to the physical fluid dynamics. Then, the infrared channel (thermodynamic information) is incorporated to post-process the cloud tracking results in order to capture the cloud density variations and small details of cloud fluidity. Experimental results on GOES (Geostationary Operational Environmental Satellite) cloud image sequences are presented in order to validate and evaluate both the effectiveness and robustness of our algorithm.
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